با سلام خدمت کاربران عزیز، به اطلاع می رساند ترجمه مقالاتی که سال انتشار آن ها زیر 2008 می باشد رایگان بوده و میتوانید با وارد شدن در صفحه جزییات مقاله به رایگان ترجمه را دانلود نمایید.
MISS-D: A fast and scalable framework of medical image storage service based on distributed file system
MISS-D: یک چارچوب سریع و مقیاس پذیر از خدمات ذخیره سازی تصویر پزشکی بر اساس سیستم فایل توزیع شده-2020
Background and Objective Processing of medical imaging big data is deeply challenging due to the size of data, computational complexity, security storage and inherent privacy issues. Traditional picture archiving and communication system, which is an imaging technology used in the healthcare industry, generally uses centralized high performance disk storage arrays in the practical solutions. The existing storage solutions are not suitable for the diverse range of medical imaging big data that needs to be stored reliably and accessed in a timely manner. The economical solution is emerging as the cloud computing which provides scalability, elasticity, performance and better managing cost. Cloud based storage architecture for medical imaging big data has attracted more and more attention in industry and academia. Methods This study presents a novel, fast and scalable framework of medical image storage service based on distributed file system. Two innovations of the framework are introduced in this paper. An integrated medical imaging content indexing file model for large-scale image sequence is designed to adapt to the high performance storage efficiency on distributed file system. A virtual file pooling technology is proposed, which uses the memory-mapped file method to achieve an efficient data reading process and provides the data swapping strategy in the pool. Result The experiments show that the framework not only has comparable performance of reading and writing files which meets requirements in real-time application domain, but also bings greater convenience for clinical system developers by multiple client accessing types. The framework supports different user client types through the unified micro-service interfaces which basically meet the needs of clinical system development especially for online applications. The experimental results demonstrate the framework can meet the needs of real-time data access as well as traditional picture archiving and communication system. Conclusions This framework aims to allow rapid data accessing for massive medical images, which can be demonstrated by the online web client for MISS-D framework implemented in this paper for real-time data interaction. The framework also provides a substantial subset of features to existing open-source and commercial alternatives, which has a wide range of potential applications.
Keywords: Hadoop distributed file system | Data packing | Memory mapping file | Message queue | Micro-service | Medical imaging
Method for tracking and communicating aggregate risk through the use of model-based systems engineering (MBSE) tools
روش ردیابی و برقراری ارتباط ریسک با استفاده از ابزارهای مهندسی سیستم مبتنی بر مدل (MBSE)-2020
Large, complex projects can identify a significant number and variety of risks, throughout the project life cycle. These risks are analyzed, mitigated, closed or accepted as independent uncertainties. Once closed or accepted, it is easy for projects to lose awareness of their impact. In reality, each of these risks contributes some amount to the overall risk posture of the project. The ability to track and effectively communicate this aggregate risk has represented a challenge to project management. There have been previous attempts to create a schema to communicate the aggregate effect of risks, without notable success. Most of these attempts have centered on some additive metric derived from the scoring of likelihood and consequence values. This, in and of itself, is a logical approach, but all too often the scores were then aggregated to a level where all context was lost. One weakness has been a lack of attempt to create linkages or logical groups of the risks upon which useful aggregation could then occur. The overall move to model-based (systems) engineering (MBSE) has opened up a vast frontier of opportunities to better integrate all project data. MBSE provides an underlying layer that links data items to each other. Objectives link to requirements, which then link to functions, functions to physical architecture items, and so on, as far down as projects want to model. While it started with a focus on modeling requirements based on things like use cases, efforts are now underway to integrate safety and mission assurance (S&MA) information and analyses, such as risks. This effort, called Model Based Mission Assurance (MBMA), is yielding models that are more useful and are a more accurate representations of the systems. MBSE models, with this ability to link related items, provide a new means of tracking and communicating ag- gregate risks. In the proposed method, risks are added into the models as distinct items, having attributes that communicate a scoring derived from the likelihood and consequence values as charted on the standard NASA 5 ×5 risk matrix. Like earlier efforts, each box in the 5 ×5 has an associated scoring, which may include both a current score and potential post-mitigation/control score. The risk items are then linked to elements of the model, such as system objectives/goals, requirements, functions, or physical architecture items, with “Risk to ”relationships. These risks will then be communicated by use of reports generated from the model, detailing all risks and/or hazards linked to model elements. These reports can include aggregate impacts, including a current scoring and potential future state scoring based on the planned mitigations and/or controls. These reports will show all risks, open, accepted, and closed, linked to project objectives or requirements. When run as part of an upcoming risk acceptance discussion, these reports will serve to remind the team of all previous risks that relate to the effected portion of the system. When included as part of periodic program or project reviews, risk reviews, and safety reviews, this method can improve the overall understanding of the system’s true risk posture. This proposed method takes full advantage of the advances that modern modeling techniques provide, with a minimal investment of additional time. Utilizing the model environment also enables a near constant access to current state of aggregate risks.
Modeling of forward osmosis process using artificial neural networks (ANN) to predict the permeate flux
مدل سازی فرآیند اسمزوز رو به جلو با استفاده از شبکه های عصبی مصنوعی (ANN) برای پیش بینی شار نفوذ-2020
Artificial neural networks (ANN) are black box models that are becoming more popular than transport-based models due to their high accuracy and less computational time in predictions. The literature shows a lack of ANN models to evaluate the forward osmosis (FO) process performance. Therefore, in this study, a multi-layered neural network model is developed to predict the permeate flux in forward osmosis. The developed model is tested for its generalization capability by including lab-scale experimental data from several published studies. Nine input variables are considered including membrane type, the orientation of membrane, molarity of feed solution and draw solution, type of feed solution and draw solution, crossflow velocity of the feed solution, and the draw solution and temperature of the feed solution and the draw solution. The development of optimum network architecture is supported by studying the impact of the number of neurons and hidden layers on the neural network performance. The optimum trained network shows a high R2 value of 97.3% that is the efficiency of the model to predict the targeted output. Furthermore, the validation and generalized prediction capability of the model is tested against untrained published data. The performance of the ANN model is compared with a transport-based model in the literature. A simple machine learning technique such as a multiple linear regression (MLR) model is also applied in a similar manner to be compared with the ANN model. ANN demonstrates its ability to form a complex relationship between inputs and output better than MLR.
Keywords: Artificial neural network | Forward osmosis | Water treatment | Desalination | Machine learning
From chemical structure to quantitative polymer properties prediction through convolutional neural networks
از ساختار شیمیایی گرفته تا پیش بینی کمی از خواص پلیمر از طریق شبکه های عصبی در هم تنیده -2020
In this work convolutional-fully connected neural networks were designed and trained to predict the glass transition temperature of polymers based only on their chemical structure. This approach has shown to successfully predict the Tg of unknown polymers with average relative errors as low as 6%. Several networks with different architecture or hiperparameters were successfully trained using a previously studied glass transition temperatures dataset for validation, and then the same method was employed for an extended dataset, with larger Tg dispersion and polymer’s structure variability. This approach has shown to be accurate and reliable, and does not require any time consuming or expensive measurements and calculations as inputs. Furthermore, it is expected that this method can be easily extended to predict other properties. The possibility of predicting the properties of polymers not even synthesized will save time and resources for industrial development as well as accelerate the scientific understanding of structure-properties relationships in polymer science.
Keywords: QSPR | Properties prediction | Deep learning | Neural network | Smart design
Internet of Things: Evolution and technologies from a security perspective
اینترنت اشیاء: تکامل و فناوری ها از دیدگاه امنیتی-2020
In recent years, IoT has developed into many areas of life including smart homes, smart cities, agriculture, offices, and workplaces. Everyday physical items such as lights, locks and industrial machineries can now be part of the IoT ecosystem. IoT has redefined the management of critical and non-critical systems with the aim of making our lives more safe, efficient and comfortable. As a result, IoT technology is having a huge positive impact on our lives. However, in addition to these positives, IoT systems have also attracted negative attention from malicious users who aim to infiltrate weaknesses within IoT systems for their own gain, referred to as cyber security attacks. By creating an introduction to IoT, this paper seeks to highlight IoT cyber security vulnerabilities and mitigation techniques to the reader. The paper is suitable for developers, practitioners, and academics, particularly from fields such as computer networking, information or communication technology or electronics. The paper begins by introducing IoT as the culmination of two hundred years of evolution within communication technologies. Around 2014, IoT reached consumers, early products were mostly small closed IoT networks, followed by large networks such as smart cities, and continuing to evolve into Next Generation Internet; internet systems which incorporate human values. Following this evolutionary introduction, IoT architectures are compared and some of the technologies that are part of each architectural layer are introduced. Security threats within each architectural layer and some mitigation strategies are discussed, finally, the paper concludes with some future developments.
Keywords: IoT | Internet of Things | Security | Cyber security | Secure by Design | Next Generation Internet | Smart city | Sustainable city | Energy reduction | Building Energy Management Systems
Fatigue life prediction of metallic materials considering mean stress effects by means of an artificial neural network
پیش بینی طول عمر خستگی مواد فلزی با توجه به میانگین اثرات استرس با استفاده از شبکه عصبی مصنوعی-2020
The mean stress effect plays an important role in the fatigue life predictions, its influence significantly changes high-cycle fatigue behaviour, directly decreasing the fatigue limit with the increase of the mean stress. Fatigue design of structural details and mechanical components must account for mean stress effects in order to guarantee the performance and safety criteria during their foreseen operational life. The purpose of this research work is to develop a new methodology to generate a constant life diagram (CLD) for metallic materials, based on assumptions of Haigh diagram and artificial neural networks, using the probabilistic Stüssi fatigue S-N fields. This proposed methodology can estimate the safety region for high-cycle fatigue regimes as a function of the mean stress and stress amplitude in regions where tensile loading is predominance, using fatigue S-N curves only for two stress R-ratios. In this approach, the experimental fatigue data of the P355NL1 pressure vessel steel available for three stress R-ratios (−1, −0.5, 0), are used. A multilayer perceptron network has been trained with the back-propagation algorithm; its architecture consists of two input neurons (σm, N) and one output neuron (σa). The suggested CLD based on trained artificial neural network algorithm and probabilistic Stüssi fatigue fields applied to dog-bone shaped specimens made of P355NL1 steel showed a good agreement with the high-cycle fatigue experimental data, only using the stress R-ratios equal to 0 and −0.5. Furthermore, a procedure for estimating the fatigue resistance reduction factor, Kf , for the fatigue life prediction of structural details (stress R-ratios equal to 0, 0.15 and 0.3) in extrapolation regions is suggested and used to generate the Kf results for stress R-ratios from −1 to 0.3, based on machine learning artificial neural network algorithm.
Keywords: Fatigue | Artificial neural network | Back-propagation algorithm | Stüssi model | Constant life diagram
Risk propagation and mitigation of design change for complex product development (CPD) projects based on multilayer network theory
انتشار ریسک و کاهش تغییر طراحی برای پروژه های پیچیده توسعه محصول (CPD) بر اساس تئوری شبکه چند لایه-2020
Change risk propagation caused by unexpected design change has become a major source of risk to complex product development (CPD) projects and provides great challenge to existing project risk management methods. The aim of this paper is to reveal the law of design change risk propagation and mitigate the disruptive design change risk propagation through improving the robustness and resilience of CPD projects. First, the CPD projects are represented by multilayer networks with product-layer and organization-layer. Then, the model of design change risk propagation is built based on load-capacity model under the consideration of change risk mitigation strategies including redundant resource, inter-partner coordination, and project learning efficiency. Furthermore, the effects of these strategies and their portfolios are estimated through numerical simulation. The results provide the most cost-effective strategies to ultimately recover the multilayer CPD network within a certain time frame. This paper proposes a new perspective to mitigate the design change risk of CPD projects after the definition of project architecture and have implications for project managers to make reasonable decisions according to concrete project contexts.
Keywords: Complex product development | Project risk management | Design change | Risk propagation | Risk mitigation
Optimal energy management for a grid connected PV-battery system
مدیریت بهینه انرژی برای سیستم باتری PV متصل به شبکه-2020
The increase demand for electricity and the non-renewable nature of fossil energy makes the move towards renewable energies required. However, the common problem of renewable sources, which is the intermittence, is overcome by the hybridization of complementary sources. Thus, whenever the load demand is not fully covered by the primary source, the second one will absolutely support it. Furthermore, the production, the interaction with the grid and the storage system must be managed by the grid-connected hybrid renewable energy system, which is the main objective of this paper. Indeed, we propose a new system of a grid-connected PV-battery, which can manage its energy flows via an optimal management algorithm. The DC bus source connection topology in our proposed hybrid architecture tackles the synchronization issues between sources when the load is powered. We consider in this work that choosing a battery discharge and charge limiting power provides an extension of the battery life. On the other hand, we simulated the dynamic behavior of the architecture’s various components according to their mathematical modeling. Following this, an energy management algorithm was proposed, and simulated using MATLAB/SIMULINK to serve the load. The results have shown that the load was served in all cases, taking into account the electrical behavior of the inhabitants as well as the weather changes on a typical day. Indeed, the load was served either by instant solar production between sunrise and sunset, or the recovery from sunset to 10pm, which could be a stored or injected energy without exceeding the 1000W per hour
Keywords: Renewable energy | PV-battery | Hybrid renewable system | Energy management | Hybrid architecture
Blockchain for Internet of Energy management: Review, solutions, and challenges
بلاکچین برای مدیریت انرژی اینترنت: بررسی ، راه حل ها و چالش ها-2020
After smart grid, Internet of Energy (IoE) has emerged as a popular technology in the energy sector by integrating different forms of energy. IoE uses Internet to collect, organize, optimize and manage the networks energy information from different edge devices in order to develop a distributed smart energy infrastructure. Sensors and communication technologies are used to collect data and to predict demand and supply by consumers and suppliers respectively. However, with the development of renewable energy resources, Electric Vehicles (EVs), smart grid and Vehicle-to-grid (V2G) technology, the existing energy sector started shifting towards distributed and decentralized solutions. Moreover, the security and privacy issues because of centralization is another major concern for IoE technology. In this context, Blockchain technology with the features of automation, immutability, public ledger facility, irreversibility, decentralization, consensus and security has been adopted in the literature for solving the prevailing problems of centralized IoE architecture. By leveraging smart contracts, blockchain technology enables automated data exchange, complex energy transactions, demand response management and Peer-to-Peer (P2P) energy trading etc. Blockchain will play vital role in the evolution of the IoE market as distributed renewable resources and smart grid network are being deployed and used. We discuss the potential and applications of blockchain in the IoE field. This article is build on the literature research and it provides insight to the end-user regarding the future IoE scenario in the context of blockchain technology. Lastly this article discusses the different consensus algorithm for IoE technology.
Keywords: Consensus algorithm | Blockchain | Internet of Energy | Smart grid | Vehicle-to-grid
The design of software development platform for CFETR plasma control system
طراحی بستر توسعه نرم افزار برای سیستم کنترل پلاسما CFETR-2020
The Plasma Control System (PCS) is a critical system of the tokamak device to guarantee the physical experiment operation. While the Chinese Fusion Engineering Testing Reactor (CFETR) PCS is in the preliminary development stage, the newly designed Plasma Control System Software Development Platform (PCS-SDP) will provide an effective, convenient, and visual development environment for PCS software developers. The PCS-SDP is developed based on the Eclipse framework as an extension and finally realized as an Eclipse plug-in. It is deployed in a thin-client C/S mode in which developers log in and work remotely and all the developments are carried on a development server. The PCS-SDP possesses an intuitive UI and contains modules of project management, algorithm management, visualization management, testing management, and version management. Because of these customized functions, the PCS-SDP makes the developers focus on the control logic design of the PCS algorithms without the need to pay attention to the PCS details; the work efficiency is improved significantly. In this paper, the requirements are analyzed, the system architecture and module design are presented, and some functions are demonstrated. The initial hardware environment deployment has been implemented and is also presented in this paper. Further efforts will be made to implement and demonstrate the functions of all modules on the EAST PCS, then serve CFETR PCS development and can be appropriate for most Plasma Control Systems
Keywords: Software platform | Plasma control system | Eclipse | Visualization | Algorithm management